MaskedArray.ids() [source] Return the addresses of the data and mask areas. Parameters: None Examples >>> x = np.ma.array([1, 2, 3], mask=[0, 1, 1]) >>> x.ids() (166670640, 166659832) If the array has no mask, the address of nomask is returned. This address is typically not close to the data in memory: >>> x = np.ma.array([1, 2, 3]) >>> x.ids() (166691080, 3083169284L)

MaskedArray.argmin(axis=None, fill_value=None, out=None) [source] Return array of indices to the minimum values along the given axis. Parameters: axis : {None, integer} If None, the index is into the flattened array, otherwise along the specified axis fill_value : {var}, optional Value used to fill in the masked values. If None, the output of minimum_fill_value(self._data) is used instead. out : {None, array}, optional Array into which the result can be placed. Its type is preserved a

ndarray.argmin(axis=None, out=None) Return indices of the minimum values along the given axis of a. Refer to numpy.argmin for detailed documentation. See also numpy.argmin equivalent function

recarray.argmin(axis=None, out=None) Return indices of the minimum values along the given axis of a. Refer to numpy.argmin for detailed documentation. See also numpy.argmin equivalent function

recarray.diagonal(offset=0, axis1=0, axis2=1) Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed. Refer to numpy.diagonal for full documentation. See also numpy.diagonal equivalent function

numpy.ma.cumsum(self, axis=None, dtype=None, out=None) = Return the cumulative sum of the elements along the given axis. The cumulative sum is calculated over the flattened array by default, otherwise over the specified axis. Masked values are set to 0 internally during the computation. However, their position is saved, and the result will be masked at the same locations. Parameters: axis : {None, -1, int}, optional Axis along which the sum is computed. The default (axis = None) is to co

recarray.prod(axis=None, dtype=None, out=None, keepdims=False) Return the product of the array elements over the given axis Refer to numpy.prod for full documentation. See also numpy.prod equivalent function

numpy.ma.hsplit(ary, indices_or_sections) = Split an array into multiple sub-arrays horizontally (column-wise). Please refer to the split documentation. hsplit is equivalent to split with axis=1, the array is always split along the second axis regardless of the array dimension. See also split Split an array into multiple sub-arrays of equal size. Notes The function is applied to both the _data and the _mask, if any. Examples >>> x = np.arange(16.0).reshape(4, 4) >>>

MaskedArray.swapaxes(axis1, axis2) [source] Return a view of the array with axis1 and axis2 interchanged. Refer to numpy.swapaxes for full documentation. See also numpy.swapaxes equivalent function

MaskedArray.__isub__(other) [source] Subtract other from self in-place.